Methods for deriving and calibrating privacy-preserving heat maps from mobile sports tracking application data

Abstract

Utilization of movement data from mobile sports tracking applications is affected by its inherent biases and sensitivity, which need to be understood when developing value-added services for, e.g., application users and city planners. We have developed a method for generating a privacy-preserving heat map with user diversity (ppDIV), in which the density of trajectories, as well as the diversity of users, is taken into account, thus preventing the bias effects caused by participation inequality. The method is applied to public cycling workouts and compared with privacy-preserving kernel density estimation (ppKDE) focusing only on the density of the recorded trajectories and privacy-preserving user count calculation (ppUCC), which is similar to the quadrat-count of individual application users. An awareness of privacy was introduced to all methods as a data pre-processing step following the principle of k-Anonymity. Calibration results for our heat maps using bicycle counting data gathered by the city of Helsinki are good (R 2 N 0.7) and raise high expectations for utilizing heat maps in a city planning context. This is further supported by the diurnal distribution of the workouts indicating that, in addition to sports-oriented cyclists, many utilitarian cyclists are tracking their commutes. However, sports tracking data can only enrich official in-situ counts with its high spatio-temporal resolution and coverage, not replace them.

GIS and Transport Modeling—Strengthening the Spatial Perspective

“…Rossi et al [57] demonstrated how sensitive GPS trajectories are in terms of privacy, while de Montjoye et al [58] proved that even coarsened movement data provide information about individuals. Oksanen et al [59], therefore, proposed a method to preserve privacy in mobility hotspot maps.…”

Conflation of OpenStreetMap and Mobile Sports Tracking Data for Automatic Bicycle Routing

“…The tracking data provided by Sports Tracker (http://www.sports-tracker.com) has previously been used to investigate city dynamics (Ferrari and Mamei 2013) and to create heat maps which enable cyclists to compare potential routes by means of visual data mining (Oksanen et al 2015). The main objective of our study was to extend the aforementioned work by providing the user with automatic popularity-based routing in a street network, by combining recorded workout trajectories with cycling-specific network data extracted from OpenStreet-Map (OSM: openstreetmap.org).…”

Modelling Spatial Patterns of Outdoor Physical Activities Using Mobile Sports Tracking Application Data

“…Mamei (2011, 2013) have used Nokia Sports Tracker application data to identify the areas and temporal routines of a city most used for a given sports activity, highlight cultural and climate-related differences among cities and show differences in the routine behaviour of various demographic and social communities. Oksanen et al (2015) aim to extract frequently used routes from massive public workout data in order to define the most popular routes as a suggestion for application users.…”